AI Image
AI Text
-
Coming Soon
Your app tells you the contacts table is empty. It doesn't tell you which names are safe to show — or how much risk is sitting in a screenshot that still has a real phone number.
Every product team has a contacts list. Almost none of them have a safe dataset. Managers don't act on "we'll make something up" — they act on numbers: "5 complete fake profiles, ready for the walkthrough" is a decision. "Just invent a few names" is not. That gap — between needing realistic people and being allowed to use real people — is exactly what a Synthetic Data Generator Agent closes. Not a chatbot that invents a name and hopes the email validates. An agent that calls random_users and returns complete fake profiles: name, gender, location, email, login, date of birth, phone, picture, and nationality. Ask for 1, 5, or 10. Drop them into Storybook, Cypress, a mock CRM, or an agent walkthrough. This isn't a nicer dummy-data spreadsheet. It's the difference between finding out a real customer was in the deck after it was shared, and filling the screen with fake people before anyone ever asked for production.
Stop thinking of it as a fake-name picker. Think of it as a standing analyst that never sleeps on your need for people-shaped data.
A Synthetic Data Generator Agent, built the right way, gives you:
Not a dump of invented names in a chat, but where every fake person stands on this call: complete records — name, gender, location, email, login, dob, phone, picture, nationality.
No black box paragraph. Click any profile and see exactly which fields came back: title / first / last, street-city-country, synthetic email, login hashes, avatar URLs at large, medium, and thumbnail.
The agent flags the empty state before someone pastes a real CRM row. Ask for 1, 5, or 10+ profiles. By the time a real phone number is in the screenshot, that moment is gone.
Not "this looks like a user," but name, email, phone, photo, location, login — fields your forms will actually accept, because they come from a dedicated generator, not a bluff.
Not every invented persona, the right count: pass results, get that many profiles back. A name you typed by hand doesn't need a tool. A table that needs 10 believable people does.
"Generated 5 random users: Ada, Ravi, Mei, …" The kind of preview that gets a demo approved — or a "don't use production PII" rule actually followed.
This is what separates an intelligence agent from a reporting tool: it doesn't just tell you the contacts table is empty. It tells you who to put there, how many, that they're fake, and what to do about it before the next real customer shows up in a screenshot.
This isn't built for data scientists. It's built for the people who are tired of finding out a real customer was in the demo after the deck already went out.
If you're looking for a chatbot to invent names, or for real people, scraped contacts, or anything that could identify a living person, this isn't that, and it won't pretend to be. This is for people who make decisions based on which fake profiles are safe to put on a screen today — not people who want a nicer way to paste yesterday's CRM export.
| Generic AI Agents / Chatbots | Us | |
|---|---|---|
| Consistency | Different answer each time you ask — probabilistic, hard to trust twice | Structured profiles from random_users — same shape, every call: name, email, location, phone, picture |
| Cost to run | Re-processes your entire request through an LLM on every query | Purpose-built generator, minimal token usage — cheap enough to run on every demo; ask for N profiles, get N profiles back |
| Reliability | Novel/untested logic, confidently wrong on edge cases — missing fields, invalid emails | Complete records from a dedicated API — hardened against the "invent a user" breakage that fails your signup form |
| Data privacy | Your data often leaves your systems, sits with a third party — tempting to paste real CRM rows into a chat | Full SOC 2 compliance — fake people only; no real customer data required, ever. Auth injects your user id; you never pass it |
In short: Other agents give you a plausible-sounding person. This gives you a profile you can defend, reproduce, and put in front of your security team without a fight.
We didn't ask a language model to invent a person and hope your forms behave. Everything underneath is a structured synthetic record — the same fields product, QA, and agent workflows actually need, without a real customer book.
| What It Does | Field |
|---|---|
| Gives you a display name that looks like a real person in a UI — title, first, last | Name |
| Fills address and locale UI: street, city, state, country, postcode, coordinates, timezone | Location |
| Passes email-shaped validation in signup and CRM screens — plus phone and cell | Email / phone |
| Supplies uuid, username, password, salt, and hashes for demo logins and fixtures | Login |
| Returns large, medium, and thumbnail image URLs — plus date of birth, gender, and nationality | Picture + identity |
Why this matters to you: Every field here is generated as synthetic data for demos and testing — not copied from a real customer book. We didn't ask an LLM to "estimate a user." We return a structured fake profile from random_users, the same way a fixture file would — reproducible in shape, defensible in a security review, and the same schema every time you ask.
That's what "synthetic" actually means. Not a marketing word. A privacy word.
ChatGPT and other LLMs are built to talk. Ask them a question, and they generate the most statistically likely-sounding answer based on patterns in text — which is exactly why they're brilliant at drafting a persona paragraph and unreliable at giving you a user record your signup form will accept.
Here's the practical difference:
random_users tool with name, gender, location, email, login, dob, phone, picture, and nationality. Ask twice, get two different shapes.In short: ChatGPT can help you describe a persona. It can't reliably tell you which fields to put in the table, how many people, or what it's worth if you paste a real customer instead. That's the gap this agent exists to close.
In plain terms — it looks at how many fake people you need and does three things on every run:
results (minimum 1), get that many complete fake people, not a gut feeling or a stale spreadsheet of invented names.In short: It turns an empty contacts table into a to-do list, with a fake person next to every row, so you know exactly where a demo is safe to show.
No — this isn't an engineering project. If you can connect an MCP server in Claude or Cursor (and every one of them can), you already have everything the agent needs. You don't even have to name the columns. Name, email, location, phone, picture — it returns the fields as they are.
Just follow the step-by-step tutorials we provide, and you're up and running in minutes — not weeks, not a data science team, not an IT ticket. No integrations to configure, no models to train yourself, no code to write.
In short: If you can ask for N users, you can have this running today. Follow the step-by-step tutorial.
Yes. This is built to be fully SOC 2 compliant, and you never have to send real customer data in the first place.
There's no export of your customer book, no black-box vendor holding production names and phones, no "trust us" — the profiles are synthetic. These are fake people for demos and testing, not real PII. Your authenticated user id is injected server-side; you never pass it. For a business, mixing real contacts into a demo is sensitive by definition; this was built with that as a requirement from day one, not an afterthought bolted on later.
In short: You get the profiles, not the exposure.
It's based on a dedicated random-users generator that has been built to return structured fake records — not a new prompt hoped to invent valid emails.
The generator looks at three things about every request: how many profiles you asked for (results), whether Pro is required (isPro), and the authenticated user injected server-side. From that, it returns complete synthetic people — name, location, email, login, dob, phone, picture, nationality — the same category of fixture data used in demos and QA, refined so a form can actually load it. If you only asked for one field (a name, an avatar), it still returns a full profile so you can pick what you need.
In short: It's not guessing based on patterns in text, like a general AI chatbot would. It's generating, using a schema that's had the time to prove it loads.
Non-deterministic means: ask the same question twice, get two different answers. This is how most general AI chatbots work — they generate the most likely-sounding response each time, which can shift even when nothing about your request changed. Fine for writing a birthday message. Risky when the "answer" is a user record your test suite has to load, or a name that must not be a real customer.
Deterministic means: same schema in, same shape out — every single time. No missing fields, no drift, no "it felt like a different email format today." If the people change, it's because you asked for new synthetic profiles — not because the model rolled different dice on which columns to include.
What we use: A dedicated generator, every time. The profiles this agent gives you come from random_users, not a language model guessing at a plausible person. That's what lets you show a demo to your security team, your QA lead, or your own boss and know the records will hold up if someone asks for ten more tomorrow.
In short: We don't give you an answer you have to hope is right. We give you the same, defensible schema, every time you ask.
Ready to Stop Using Real Customers You Didn't Mean to Expose?
You already have the workflow. Your demo, QA suite, and agent need people-shaped data — you just need to know how to connect the Random Users MCP. We'll walk you through exactly how, step by step.
Open the Tutorial →No credit card. No production dump. Just fake people and a few minutes.
